CSC 830: Distributed Systems

This course provides a comprehensive exploration of distributed systems, focusing on the principles, design, and implementation of large-scale software systems across multiple computers. Students will gain a deep understanding of the fundamental challenges associated with distributed systems and explore techniques for achieving reliability, scalability, and consistency.

Course Objectives

Upon completion of this course, you should be well-prepared to:

1. Explain the core concepts and challenges in distributed systems design.
2. Analyze different distributed system architectures and their trade-offs.
3. Implement distributed applications using inter-process communication mechanisms.
4. Apply techniques for achieving consistency and fault tolerance in distributed systems.
5. Design and evaluate solutions for distributed coordination and consensus.
6. Understand the impact of time synchronization on distributed systems.
7. Discuss advanced topics in distributed systems, such as distributed databases and cloud computing.

Course Topics

This course contains the following lessons and topics.

Lesson 1: Introduction to Distributed Systems

• Overview of distributed systems: motivations, characteristics, and examples.
• Distributed computing paradigms: client-server, peer-to-peer, cloud computing.
• Fundamental challenges: consistency, fault tolerance, scalability, security.
• Communication patterns: synchronous vs. asynchronous, message passing.

Lesson 2: Architectures for Distributed Systems

• Client-server architectures: advantages, disadvantages, and variations.
• Peer-to-peer architectures: decentralized systems, structured vs. unstructured.
• Replicated systems: data replication strategies, consistency challenges.
• Microservices and containerization in distributed systems.

Lesson 3: Inter-Process Communication (IPC)

• Sockets: low-level network communication.
• Remote Procedure Calls (RPC): principles, implementation, and middleware.
• Message queues: asynchronous communication, message brokers.
• Comparison of IPC mechanisms: performance, complexity, use cases.

Lesson 4: Clock Synchronization and Global State

• The importance of clock synchronization in distributed systems.
• Physical clock synchronization: Network Time Protocol (NTP).
• Logical clocks: Lamport clocks, vector clocks.
• Global state: snapshot algorithms, consistent cuts.

Lesson 5: Consistency Models and Replication

• Consistency models: strict consistency, sequential consistency, eventual consistency.
• Data replication: techniques, trade-offs, and consistency maintenance.
• Quorum-based replication.
• Consistency and performance trade-offs.

Lesson 6: Consensus

• The consensus problem: definition and importance.
• Paxos algorithm: detailed explanation and variations.
• Raft algorithm: a more understandable consensus algorithm.
• Practical considerations: fault tolerance, performance.

Week 7: Distributed Transactions

• Distributed transactions: ACID properties in distributed environments.
• Two-phase commit (2PC): protocol, limitations, and alternatives.
• Three-phase commit (3PC).
• Optimistic concurrency control.

Lesson 8: Distributed File Systems

• Design principles of distributed file systems.
• Case study: Google File System (GFS) or Hadoop Distributed File System (HDFS).
• File consistency and data replication in DFS.
• Performance considerations and optimizations.

Lesson 9: Distributed Databases

• Distributed database architectures: sharding, replication, and combinations.
• Data partitioning and distribution strategies.
• Distributed query processing and optimization.
• NoSQL databases and their role in distributed systems.

Lesson 10: Fault Tolerance and Reliability

• Failure models and fault tolerance techniques.
• Redundancy and replication strategies.
• Failure detection and recovery mechanisms.
• Disaster recovery and business continuity.

Lesson 11: Security in Distributed Systems

• Security challenges in distributed environments.
• Authentication and authorization mechanisms.
• Data encryption and secure communication.
• Denial-of-service attacks and mitigation strategies.

Lesson 12: Cloud Computing and Distributed Systems

• Cloud computing paradigms: IaaS, PaaS, SaaS.
• Cloud-native architectures and microservices.
• Serverless computing and distributed functions.
• Cloud-based distributed systems: case studies.

Lesson 13: Monitoring and Management of Distributed Systems

• Monitoring distributed systems: metrics, logs, and tracing.
• Performance analysis and tuning.
• Automated deployment and management tools.
• Debugging and troubleshooting distributed applications.

Lesson 14: Advanced Topics and Research Directions

• Edge computing and its impact on distributed systems.
• Blockchain and distributed ledger technologies.
• Research trends in distributed systems: open problems and future directions.

Course Requirements and Grading

This course contains ungraded Knowledge Checks, Knowledge in Practice applications, and other interactive activities. Though these interactions are not graded, they are designed to help you learn. By engaging in ungraded activities, you will be better prepared for graded activities in the course.

The course contains the following graded assignments in the following categories:

• Participation (10%): Embedded knowledge checks and practical application questions allow you the opportunity to test your knowledge and application skills as you progress.
• Quizzes (20%): Quizzes test how well the reader has understood the material they have studied.
• Assignments (70%): Assignments allow students the opportunity to demonstrate their application of knowledge.

The following table summarizes assignments and their associated values.

Course Grade Distribution

Assignment Category	Number	Percent of Total Grade
Participation	14	10%
Quizzes	14	20%
Assignments	13	70%
Total	–	100%